Institutions desire to learn about an initiative's effectiveness. The institutions will continuously try to optimize the store layouts and arrangements in order to maximize their profit and predict customer performance given different scenarios. The institutions often attempt to maximize their sales and profit by optimizing the space within the store dedicated to certain commodities. In one conventional example, a retailer owns multiple stores that sell a variety of products. Conventional theories posit that a store will sell more commodities if the store displays more of that commodity (e.g., the store dedicates more space to said commodity). However, the concept of diminishing return explains that blindly increasing the space dedicated to a product is not necessarily the best solution to maximize profits. The diminishing return concept is the decrease in the marginal (incremental) output of a production process as the amount of a single factor of production is incrementally increased, while the amounts of all other factors of production stay constant. For example, continuing with the example above, if the retailer increases the space from 5% of the shelving units to 10%, the store may see a 100% increase in sales (e.g., sales may double); however, if the same retailer increases the space dedicated to the same product to 20% of all the shelving units, the store may not experience a 400% increase in sales (e.g., sales may not quadruple). Simply put, the law of diminishing returns states that in all productive processes, increasing one factor of production, while holding all others constant (“ceteris paribus”), will at some point (e.g., diminishing return point) yield lower incremental per-unit returns.
Conventional approaches to optimizing a space within a store based on customer behavior have been accomplished using a “trial-and-error” method of modifying the spaces and studying customer behavior utilizing “brute force” methods, such as analyzing sales in relation to the space allocated to products. For example, an institution may allocate more space to a product, analyze the sales associated with said product for a pre-determined period of time, and depending on the analysis, change the space allocated to the product and re-analyze the sales. As expected, this process is tedious and time consuming. The “trial-and-error” method is also inaccurate because many other factors associated with sales (e.g., seasonality, utility, or demand) may change throughout the analysis, which may yield unexpected and imprecise results. Furthermore, the “trial-and-error” method may not be suitable because it heavily relies on human subjectivity (e.g., the amount of space and/or the price are selected by the analyzers).
As the processing power of computers allow for greater computer functionality and the Internet technology era allows for interconnectivity between computing systems, many institutions use computers to optimize retail space. However, since the implementation of these more sophisticated online tools, several shortcomings in these technologies have been identified and have created a new set of technical challenges. Several existing and conventional software solutions provide the same “trial-and-error” method implemented on computing devices and fail to provide fast and efficient analysis due to a high volume of customer/store information existing on different networks and computing infrastructures. Managing such information on different platforms is difficult due to number, size, content, or relationships of the data associated with the customers. For example, optimizing space for a store that provides several products may entail calculating millions or billions of different and distinct combinations of sales and space. Conventional software solutions may take hours or even days to complete the analysis because there is often not enough processing power and time to search and analyze all different combination of the spaces, sales prices, and diminishing return values allocated to each product. Furthermore, many existing and conventional graphical user interfaces do not illustrate the optimized data (e.g., optimized spaces and projected sales trends) in a user-friendly manner. For example, conventional software solutions may produce large spreadsheets or large graphs comprising confusing data points.